Image registration plays an important role in various multi-image analysis tasks such as change detection, image fusion, image restoration, etc. Image registration is the process of overlaying multiple images of the same object or scene taken at different times, from different viewpoints and/or by different sensors. To achieve the desired overlaying of multiple images, an arbitrary pair of the multiple images is aligned by applying a transformation rule reflective of a spatial relationship therebetween, where the transformation rule is used to transform one of the two images to the coordinate system of the other image.
Image registration has various applications. For example, in medicine, image registration may be used to overlay an image of a patient obtained from a sensor over a predetermined template image for diagnosis purposes (i.e., transform the acquired image to the coordinate system of the template image and overlay the transformed image onto the template image). Image registration enables an accurate comparison of the acquired image with respect to the template image and diagnosis of illness or disorder. In another example, multiple images of a scene taken from different perspectives may be transformed into the coordinate system of a selected reference image, so as to form a single mosaic image of the scene that is observed from the perspective of the reference image.
There are a large number of conventional image registration methods, such as area-based methods (e.g., cross-correlation methods, Fourier methods, mutual information methods, optimization methods, etc.) and feature-based methods (e.g., spatial relations methods, invariant descriptors methods, relaxation methods, etc.). However, there are only a handful of methods capable of objectively evaluating the performance or accuracy of these image registration algorithms. Many of these evaluation methods involve preparing, for example, a calibration (or control) data set and comparing how well the image registration algorithm produces accurate results in view of the calibration data set, which are not only time consuming but also costly to perform. Further, they do not provide an objective measure of the accuracy of a given image registration algorithm. This is particularly so as the number of images to be registered and then evaluated increases.